An integrated proteomics and metabolomics approach for defining oncofetal biomarkers in the colorectal cancer. Ann Surg. 2012;255:720-730. [PMID: 22395091 DOI: 10.1097/sla.0b013e31824a9a8b]

Metabolomic analysis of the saliva of Japanese patients with oral squamous cell carcinoma

The aim of the present study was to characterize the metabolic systems in Japanese patients with oral squamous cell carcinoma (OSCC) using capillary electrophoresis-mass spectrometry (CE-MS) metabolome analysis of saliva samples. A previous study showed variations among ethnicities and tumor sites in the saliva metabolome of patients with OSCC using CE-MS. In the present study, saliva was obtained from 22 Japanese patients with OSCC and from 21 healthy controls who visited the Department of Dentistry, Oral and Maxillofacial Surgery, Tokyo Dental Collage Ichikawa General Hospital, Tokyo, Japan, and all samples were subject to comprehensive quantitative metabolome analysis using CE-MS. A total of 499 metabolites were detected as CE-MS peaks in the saliva tested from the two groups. A total of 25 metabolites were revealed as potential markers to discriminate between patients with OSCC and healthy controls: Choline, p-hydroxyphenylacetic acid, and 2-hydroxy-4-methylvaleric acid (P<0.001); valine, 3-phenyllactic acid, leucine, hexanoic acid, octanoic acid, terephthalic acid, γ-butyrobetaine, and 3-(4-hydroxyphenyl)propionic acid (P<0.01); and isoleucine, tryptophan, 3-phenylpropionic acid, 2-hydroxyvaleric acid, butyric acid, cadaverine, 2-oxoisovaleric acid, N6,N6,N6-trimethyllysine, taurine, glycolic acid, 3-hydroxybutyric acid, heptanoic acid, alanine, and urea (P<0.05, according to the Wilcoxon rank sum test). A previous study by Sugimoto and co-workers detected 24 discriminatory metabolites, 7 of which (taurine, valine, leucine, isoleucine, choline, cadaverine, and tryptophan) were also detected in the present study. In the present study, however, choline, metabolites in the branched chain amino acids (BCAA) cycle, urea, and 3-hydroxybutyric acid were also characterized. Choline and metabolites of the BCAA cycle have previously been reported in OSCC using metabolome analysis. To the best of our knowledge, no previous reports have identified urea and 3-hydroxybuyric acid in the metabolome of patients with OSCC. These findings suggest the usefulness of metabolites as salivary biomarkers for Japanese patients with OSCC. Further studies using larger patient cohorts should be conducted to validate these results.

Technological advances in current metabolomics and its application in tradition Chinese medicine

During the last few years, many metabolomics technologies have been established in biomedical research for analyzing the changes of metabolite levels.

Fecal Metabolome in Hmga1 Transgenic Mice with Polyposis: Evidence for Potential Screen for Early Detection of Precursor Lesions in Colorectal Cancer

Because colorectal cancer (CRC) remains a leading cause of cancer mortality worldwide, more accessible screening tests are urgently needed to identify early stage lesions. We hypothesized that highly sensitive, metabolic profile analysis of stool samples will identify metabolites associated with early stage lesions and could serve as a noninvasive screening test. We therefore applied traveling wave ion mobility mass spectrometry (TWIMMS) coupled with ultraperformance liquid chromatography (UPLC) to investigate metabolic aberrations in stool samples in a transgenic model of premalignant polyposis aberrantly expressing the gene encoding the high mobility group A (Hmga1) chromatin remodeling protein. Here, we report for the first time that the fecal metabolome of Hmga1 mice is distinct from that of control mice and includes metabolites previously identified in human CRC. Significant alterations were observed in fatty acid metabolites and metabolites associated with bile acids (hypoxanthine xanthine, taurine) in Hmga1 mice compared to controls. Surprisingly, a marked increase in the levels of distinctive short, arginine-enriched, tetra-peptide fragments was observed in the transgenic mice. Together these findings suggest that specific metabolites are associated with Hmga1-induced polyposis and abnormal proliferation in intestinal epithelium. Although further studies are needed, these data provide a compelling rationale to develop fecal metabolomic analysis as a noninvasive screening tool to detect early precursor lesions to CRC in humans.

Molecular Biomarkers of Colorectal Cancer and Cancer Disparities: Current Status and Perspective

This review provides updates on the efforts for the development of prognostic and predictive markers in colorectal cancer based on the race/ethnicity of patients. Since the clinical consequences of genetic and molecular alterations differ with patient race and ethnicity, the usefulness of these molecular alterations as biomarkers needs to be evaluated in different racial/ethnic groups. To accomplish personalized patient care, a combined analysis of multiple molecular alterations in DNA, RNA, microRNAs (miRNAs), metabolites, and proteins in a single test is required to assess disease status in a precise way. Therefore, a special emphasis is placed on issues related to utility of recently identified genetic and molecular alterations in genes, miRNAs, and various "-omes" (e.g., proteomes, kinomes, metabolomes, exomes, methylomes) as candidate molecular markers to determine cancer progression (disease recurrence/relapse and metastasis) and to assess the efficacy of therapy in colorectal cancer in relation to patient race and ethnicity. This review will be useful for oncologists, pathologists, and basic and translational researchers.

A validated metabolomic signature for colorectal cancer: exploration of the clinical value of metabolomics

Background:

Timely diagnosis and classification of colorectal cancer (CRC) are hindered by unsatisfactory clinical assays. Our aim was to construct a blood-based biomarker series using a single assay, suitable for CRC detection, prognostication and staging.

Methods:

Serum metabolomic profiles of adenoma (N=31), various stages of CRC (N=320) and healthy matched controls (N=254) were analysed by gas chromatography-mass spectrometry (GC-MS). A diagnostic model for CRC was derived by orthogonal partial least squares-discriminant analysis (OPLS-DA) on a training set, and then validated on an independent data set. Metabolomic models suitable for identifying adenoma, poor prognosis stage II CRC and discriminating various stages were generated.

Results:

A diagnostic signature for CRC with remarkable multivariate performance (R²Y=0.46, Q²Y=0.39) was constructed, and then validated (sensitivity 85% specificity 86%). Area under the receiver-operating characteristic curve was 0.91 (95% CI, 0.87–0.96). Adenomas were also detectable (R²Y=0.35, Q²Y=0.26, internal AUROC=0.81, 95% CI, 0.70–0.92). Also of particular interest, we identified models that stratified stage II by prognosis, and classified cases by stage.

Conclusions:

Using a single assay system, a suite of CRC biomarkers based on circulating metabolites enables early detection, prognostication and preliminary staging information. External population-based studies are required to evaluate the repeatability of our findings and to assess the clinical benefits of these biomarkers.

Dietary fat overcomes the protective activity of thrombospondin-1 signaling in the Apc(Min/+) model of colon cancer

Thrombospondin 1 is a glycoprotein that regulates cellular phenotype through interactions with its cellular receptors and extracellular matrix-binding partners. Thrombospondin 1 locally regulates angiogenesis and inflammatory responses that contribute to colorectal carcinogenesis in Apc^Min/+ mice. The ability of thrombospondin 1 to regulate responses of cells and tissues to a variety of stresses suggested that loss of thrombospondin 1 may also have broader systemic effects on metabolism to modulate carcinogenesis. Apc^Min/+:Thbs1^−/− mice exhibited decreased survival and higher tumor multiplicities in the small and large intestine relative to Apc^Min/+ mice when fed a low (5%) fat western diet. However, the protective effect of endogenous thrombospondin 1 was lost when the mice were fed a western diet containing 21% fat. Biochemical profiles of liver tissue identified systemic metabolic changes accompanying the effects of thrombospondin 1 and dietary lipid intake on tumorigenesis. A high-fat western diet differentially regulated elements of amino acid, energy and lipid metabolism in Apc^Min/+:Thbs1^−/− mice relative to Apc^Min/+:Thbs1^+/+mice. Metabolic changes in ketone body and tricarboxylic acid cycle intermediates indicate functional interactions between Apc and thrombospondin 1 signaling that control mitochondrial function. The cumulative diet-dependent differential changes observed in Apc^Min/+:Thbs1^−/− versus Apc^Min/+ mice include altered amino acid and lipid metabolism, mitochondrial dysfunction, eicosanoids and ketone body formation. This metabolic profile suggests that the protective role of thrombospondin 1 to decrease adenoma formation in Apc^Min/+ mice results in part from improved mitochondrial function.

Combined Metabolomics and Proteomics Analysis of Major Depression in an Animal Model: Perturbed Energy Metabolism in the Chronic Mild Stressed Rat Cerebellum

Major depressive disorder (MDD) is a highly prevalent, debilitating mental illness of importance for global health. However, its molecular pathophysiology remains poorly understood. Combined proteomics and metabolomics approaches should provide a comprehensive understanding of MDD's etiology. The present study reports novel "-omics" insights from a rodent model of MDD. Cerebellar samples from chronic mild stressed (CMS)-treated depressed rats and controls were compared with a focus on the differentially expressed proteins and metabolites using isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomics and gas chromotography/mass spectrometry (GC-MS) metabolomics techniques, respectively. The combined analyses found significant alterations associated with cerebellar energy metabolism, as indicated by (1) abnormal amino acid metabolism accompanied by corresponding metabolic enzymatic alterations and disturbed protein turnover, (2) increased glycolytic and tricarboxylic acid (TCA) cycle enzyme levels paralleled by changes in the concentrations of associated metabolites, and (3) perturbation of ATP biosynthesis through adenosine accompanied by perturbation of the mitochondrial respiratory chain. To the best of our knowledge, this study is the first to integrate proteomics and metabolomics analyses to examine the pathophysiological mechanism(s) underlying MDD in a CMS rodent model of depression. These results can offer important insights into the pathogenesis of MDD.

Diagnostic Metabolomic Blood Tests for Endoluminal Gastrointestinal Cancer--A Systematic Review and Assessment of Quality

Advances in analytics have resulted in metabolomic blood tests being developed for the detection of cancer. This systematic review aims to assess the diagnostic accuracy of blood-based metabolomic biomarkers for endoluminal gastrointestinal (GI) cancer. Using endoscopic diagnosis as a reference standard, methodologic and reporting quality was assessed using validated tools, in addition to pathway-based informatics to biologically contextualize discriminant features. Twenty-nine studies (15 colorectal, 9 esophageal, 3 gastric, and 2 mixed) with data from 10,835 participants were included. All reported significant differences in hematologic metabolites. In pooled analysis, 246 metabolites were found to be significantly different after multiplicity correction. Incremental metabolic flux with disease progression was frequently reported. Two promising candidates have been validated in independent populations (both colorectal biomarkers), and one has been approved for clinical use. Networks analysis suggested modulation of elements of up to half of Edinburgh Human Metabolic Network subdivisions, and that the poor clinical applicability of commonly modulated metabolites could be due to extensive molecular interconnectivity. Methodologic and reporting quality was assessed as moderate-to-poor. Serum metabolomics holds promise for GI cancer diagnostics; however, future efforts must adhere to consensus standardization initiatives, utilize high-resolution discovery analytics, and compare candidate biomarkers with peer nonendoscopic alternatives.

Applying Genomics and Proteomics in Translational Surgical Oncology Research

BACKGROUND

Translational surgical research can range from investigating clinically oriented basic pathophysiological research via the correlation of molecular findings with a patient's clinical outcome to conducting treatment response studies. Herein, the specialist's opinion as a 'surgeon scientist' plays a pivotal role, e.g. in planning experimental setups focusing on the clinically most relevant needs. SUMMARY AND KEY MESSAGES: This review provides a survey of genomic and proteomic technologies and gives an up-to-date overview of those studies applying these technologies exemplarily in colorectal cancer-based biomarker research. Although current results are promising, future validation studies within multicenter networks are required to promote the translation of 'omics' from bench to bedside. In this endeavor, departments of surgery and institutes of experimental surgery together should play a fundamental role. The program for 'clinician scientists' recently launched by the German Research Society (DFG) could be one tool to promote interdisciplinary networks and surgeons' impact on translational research.

Metabolomics profiling of pre-and post-anesthesia plasma samples of colorectal patients obtained via Ficoll separation

1H NMR spectroscopy was used to investigate the metabolic consequences of general anesthesia in the plasma of two groups of patients with diagnosis for non-metastatic colorectal cancer and metastatic colorectal cancer with liver-metastasis, respectively. Patients were treated with etomidate or propofol, two frequently used sedation agents. Plasma samples were obtained via Ficoll separation. Here, we demonstrated that this procedure introduces a number of limitations for NMR-based metabolomics studies, due to the appearance of spurious signals. Nevertheless, the comparison of the 1H NMR metabolomic profiles of patients treated with etomidate or propofol at equipotent dose ranges was still feasible and proved that both agents significantly decrease the plasma levels of several NMR-detectable metabolites. Consequently, samples collected during anesthesia are not suitable for metabolic profiling studies aimed at patient stratification, because interpersonal variability are reduced by the overall depression of metabolites levels. On the other hand, this study showed that plasma metabolomics could represent a valuable tool to monitor the effect of different sedation agents and/or the individual metabolic response to anesthesia, providing hints for an appropriate tuning of personalized sedation procedures. In our reference groups, the metabolomic signatures were slightly different in patients anesthetized with etomidate versus propofol. The importance of standardized collection procedures and availability of exhaustive metadata of the experimental design for the accurate evaluation of the significance of the observed changes in metabolites levels are critically discussed.

Colorectal cancer: the potential of metabolic fingerprinting

Metabolomics is a relatively new omics science that can provide a strong individual small-molecule fingerprint. Disease onset can be monitored as a deviation from the normal healthy fingerprint at the systemic level or in tissues from the diseased organ(s). By applying mass spectrometry and nuclear magnetic resonance as analytical platforms, metabolomics has been used for colorectal cancer phenotyping at different levels. The metabolic profile as a whole is a complex biomarker of diagnostic and prognostic value. Results are promising for the implementation of the method at the clinical level, but larger scale studies and extensive standardization of the pre-analytical phase are needed for a validated definition of the colorectal cancer signature.

Metabonomics of human colorectal cancer: new approaches for early diagnosis and biomarker discovery

Colorectal cancer (CRC) is one of the most common cancers in the world, having both high prevalence and mortality. It is usually diagnosed at advanced stages due to the limitations of current screening methods used in the clinic. There is an urgent need to develop new biomarkers and modalities to detect, diagnose, and monitor the disease. Metabonomics, an approach that involves the comprehensive profiling of the full complement of endogenous metabolites in a biological system, has demonstrated its great potential for use in the early diagnosis and personalized treatment of various cancers including CRC. By applying advanced analytical techniques and bioinformatics tools, the metabolome is mined for biomarkers that are associated with carcinogenesis and prognosis. This review provides an overview of the metabonomics workflow and studies, with a focus on recent advances and findings in biomarker discovery for the early diagnosis and prognosis of CRC.

Integrated multidimensional analysis is required for accurate prognostic biomarkers in colorectal cancer

CRC cancer is one of the deadliest diseases in Western countries. In order to develop prognostic biomarkers for CRC (colorectal cancer) aggressiveness, we analyzed retrospectively 267 CRC patients via a novel, multidimensional biomarker platform. Using nanofluidic technology for qPCR analysis and quantitative fluorescent immunohistochemistry for protein analysis, we assessed 33 microRNAs, 124 mRNAs and 9 protein antigens. Analysis was conducted in each single dimension (microRNA, gene or protein) using both the multivariate Cox model and Kaplan-Meier method. Thereafter, we simplified the censored survival data into binary response data (aggressive vs. non aggressive cancer). Subsequently, we integrated the data into a diagnostic score using sliced inverse regression for sufficient dimension reduction. Accuracy was assessed using area under the receiver operating characteristic curve (AUC). Single dimension analysis led to the discovery of individual factors that were significant predictors of outcome. These included seven specific microRNAs, four genes, and one protein. When these factors were quantified individually as predictors of aggressive disease, the highest demonstrable area under the curve (AUC) was 0.68. By contrast, when all results from single dimensions were combined into integrated biomarkers, AUCs were dramatically increased with values approaching and even exceeding 0.9. Single dimension analysis generates statistically significant predictors, but their predictive strengths are suboptimal for clinical utility. A novel, multidimensional integrated approach overcomes these deficiencies. Newly derived integrated biomarkers have the potential to meaningfully guide the selection of therapeutic strategies for individual patients while elucidating molecular mechanisms driving disease progression.

Development and validation of a highly sensitive urine-based test to identify patients with colonic adenomatous polyps

Objectives:

Adenomatous polyps are precursors of colorectal cancer; their detection and removal is the goal of colon cancer screening programs. However, fecal-based methods identify patients with adenomatous polyps with low levels of sensitivity. The aim or this study was to develop a highly accurate, prototypic, proof-of-concept, spot urine-based diagnostic test using metabolomic technology to distinguish persons with adenomatous polyps from those without polyps.

Methods:

Prospective urine and stool samples were collected from 876 participants undergoing colonoscopy examination in a colon cancer screening program, from April 2008 to October 2009 at the University of Alberta. Colonoscopy reference standard identified 633 participants with no colonic polyps and 243 with colonic adenomatous polyps. One-dimensional nuclear magnetic resonance spectra of urine metabolites were analyzed to define a diagnostic metabolomic profile for colonic adenomas. A urine metabolomic diagnostic test for colonic adenomatous polyps was established using 67% of the samples (un-blinded training set) and validated using the other 33% of the samples (blinded testing set). The urine metabolomic diagnostic test's specificity and sensitivity were compared with those of fecal-based tests.

Results:

Using a two-component, orthogonal, partial least-squares model of the metabolomic profile, the un-blinded training set identified patients with colonic adenomatous polyps with 88.9% sensitivity and 50.2% specificity. Validation using the blinded testing set confirmed sensitivity and specificity values of 82.7% and 51.2%, respectively. Sensitivities of fecal-based tests to identify colonic adenomas ranged from 2.5 to 11.9%.

Conclusions:

We describe a proof-of-concept spot urine-based metabolomic diagnostic test that identifies patients with colonic adenomatous polyps with a greater level of sensitivity (83%) than fecal-based tests.

Developing proteomics-based biomarkers for colorectal neoplasms for clinical practice: opportunities and challenges

Colorectal cancer (CRC) arises from the normal colon epithelium through the accumulation of genetic mutations and epigenetic alterations that are associated with progression along the histological adenoma-adenocarcinoma sequence. Elucidating the molecular alterations underlying disease progression will not only provide insight into the behavior of the tumors, but also could lead to the discovery of useful biomarkers for diagnosis, monitoring treatment responsiveness, or predicting disease outcomes. In the past a few years, there have been several evaluating differentially expressed protein biomarkers by employing proteomics technologies coupled with mass spectrometry. In the current review, we will briefly summarize the results from selected recent studies using tissue or serum samples from CRC patients in the past 5 years and discuss the opportunities and challenges in translating these findings from the research setting to clinical practice.

Metabolomics in diagnosis and biomarker discovery of colorectal cancer

Colorectal cancer (CRC), a major public health concern, is the second leading cause of cancer death in developed countries. There is a need for better preventive strategies to improve the patient outcome that is substantially influenced by cancer stage at the time of diagnosis. Patients with early stage colorectal have a significant higher 5-year survival rates compared to patients diagnosed at late stage. Although traditional colonoscopy remains the effective means to diagnose CRC, this approach generally suffers from poor patient compliance. Thus, it is important to develop more effective methods for early diagnosis of this disease process, also there is an urgent need for biomarkers to diagnose CRC, assess disease severity, and prognosticate course. Increasing availability of high-throughput methodologies open up new possibilities for screening new potential candidates for identifying biomarkers. Fortunately, metabolomics, the study of all metabolites produced in the body, considered most closely related to a patient's phenotype, can provide clinically useful biomarkers applied in CRC, and may now open new avenues for diagnostics. It has a largely untapped potential in the field of oncology, through the analysis of the cancer metabolome to identify marker metabolites defined here as surrogate indicators of physiological or pathophysiological states. In this review we take a closer look at the metabolomics used within the field of colorectal cancer. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader.

A machine-learned predictor of colonic polyps based on urinary metabolomics

We report an automated diagnostic test that uses the NMR spectrum of a single spot urine sample to accurately distinguish patients who require a colonoscopy from those who do not. Moreover, our approach can be adjusted to tradeoff between sensitivity and specificity. We developed our system using a group of 988 patients (633 normal and 355 who required colonoscopy) who were all at average or above-average risk for developing colorectal cancer. We obtained a metabolic profile of each subject, based on the urine samples collected from these subjects, analyzed via ¹H-NMR and quantified using targeted profiling. Each subject then underwent a colonoscopy, the gold standard to determine whether he/she actually had an adenomatous polyp, a precursor to colorectal cancer. The metabolic profiles, colonoscopy outcomes, and medical histories were then analysed using machine learning to create a classifier that could predict whether a future patient requires a colonoscopy. Our empirical studies show that this classifier has a sensitivity of 64% and a specificity of 65% and, unlike the current fecal tests, allows the administrators of the test to adjust the tradeoff between the two.

Review of mass spectrometry-based metabolomics in cancer research

Metabolomics, the systematic investigation of all metabolites present within a biologic system, is used in biomarker development for many human diseases, including cancer. In this review, we investigate the current role of mass spectrometry-based metabolomics in cancer research. A literature review was carried out within the databases PubMed, Embase, and Web of Knowledge. We included 106 studies reporting on 21 different types of cancer in 7 different sample types. Metabolomics in cancer research is most often used for case-control comparisons. Secondary applications include translational areas, such as patient prognosis, therapy control and tumor classification, or grading. Metabolomics is at a developmental stage with respect to epidemiology, with the majority of studies including less than 100 patients. Standardization is required especially concerning sample preparation and data analysis. In the second part of this review, we reconstructed a metabolic network of patients with cancer by quantitatively extracting all reports of altered metabolites: Alterations in energy metabolism, membrane, and fatty acid synthesis emerged, with tryptophan levels changed most frequently in various cancers. Metabolomics has the potential to evolve into a standard tool for future applications in epidemiology and translational cancer research, but further, large-scale studies including prospective validation are needed.

Metabolomic profiling in colorectal cancer: opportunities for personalized medicine

Colorectal cancer (CRC) is one of the most common types of cancer in the world, with high prevalence and mortality. Understanding the alterations of cancer metabolism and identifying reliable biomarkers would facilitate the development of novel technologies of CRC screening and early diagnosis, as well as new approaches to providing personalized medicine. Metabolomics, as an emerging molecular phenotyping approach, provides a clinical platform technology with an unprecedented amount of metabolic readout information, which is ideal for theranostic biomarker discovery. Metabolic signatures can link the unique pathophysiological states of patients to personalized health monitoring and intervention strategies. This article presents an overview of the metabolomic studies of CRC with a focus on recent advances in the biomarker discovery in serum, urine, fecal water and tissue samples for cancer diagnosis. The development and application of metabolomics towards personalized medicine, including early diagnosis, cancer staging, treatment and drug discovery are also discussed.

A review of applications of metabolomics in cancer

Cancer is a devastating disease that alters the metabolism of a cell and the surrounding milieu. Metabolomics is a growing and powerful technology capable of detecting hundreds to thousands of metabolites in tissues and biofluids. The recent advances in metabolomics technologies have enabled a deeper investigation into the metabolism of cancer and a better understanding of how cancer cells use glycolysis, known as the “Warburg effect,” advantageously to produce the amino acids, nucleotides and lipids necessary for tumor proliferation and vascularization. Currently, metabolomics research is being used to discover diagnostic cancer biomarkers in the clinic, to better understand its complex heterogeneous nature, to discover pathways involved in cancer that could be used for new targets and to monitor metabolic biomarkers during therapeutic intervention. These metabolomics approaches may also provide clues to personalized cancer treatments by providing useful information to the clinician about the cancer patient’s response to medical interventions.

Computational tools for the secondary analysis of metabolomics experiments

Metabolomics experiments have become commonplace in a wide variety of disciplines. By identifying and quantifying metabolites researchers can achieve a systems level understanding of metabolism. These studies produce vast swaths of data which are often only lightly interpreted due to the overwhelmingly large amount of variables that are measured. Recently, a number of computational tools have been developed which enable much deeper analysis of metabolomics data. These data have been difficult to interpret as understanding the connections between dozens of altered metabolites has often relied on the biochemical knowledge of researchers and their speculations. Modern biochemical databases provide information about the interconnectivity of metabolism which can be automatically polled using metabolomics secondary analysis tools. Starting with lists of altered metabolites, there are two main types of analysis: enrichment analysis computes which metabolic pathways have been significantly altered whereas metabolite mapping contextualizes the abundances and significances of measured metabolites into network visualizations. Many different tools have been developed for one or both of these applications. In this review the functionality and use of these software is discussed. Together these novel secondary analysis tools will enable metabolomics researchers to plumb the depths of their data and produce farther reaching biological conclusions than ever before.

Periprosthetic Joint Infection: What is on the Horizon?

Periprosthetic joint infection (PJI) will emerge as one of the most important issues for both orthopedic surgeons and researchers active in the field over the coming decades. Although the rate of PJI has not changed significantly over the past decade, the affected patients (hosts) being treated often present with more comorbidities than in the past, and the organisms responsible for these infections are evolving to become more difficult to treat. Fortunately, though, major strides in basic, translational, and clinical research have occurred in recent years that have armed the clinician with an armamentarium of techniques and technologies to better diagnose, prevent, and treat PJI. Advances in diagnostics, including refinements in established biomarkers, the introduction of point of service tests, developments in molecular techniques, and new techniques in advanced imaging will allow us to correctly identify the infecting pathogens and their virulence factors. Utilizing developed risk indexes to stratify and medically optimize our patients, modifying implants to incorporate antimicrobial and anti-biofilm properties, and developing clinically applicable vaccines and biofilm inhibiting enzymes will address our struggles in preventing PJI. Success of our future treatment strategies will hinge on refining the indications and technique of our current surgical procedures as well as the rational use of biofilm disrupting technologies and photodynamic therapy. Finally, the field of metabolomics, though still relatively in its infancy, likely holds the key to a novel diagnostic and treatment approach to infection and a more profound understanding of the pathophysiology of PJI on the human body.

Short-term changes of the urine metabolome after bariatric surgery

Bariatric surgery leads to a loss of excess weight and to a remission of diabetes in a majority of patients. In an attempt to explain these underlying mechanisms, a broad range of metabolic alterations have been suggested. We aimed to investigate short-term changes in the urinary metabolome after bariatric surgery. Data for 50 patients who underwent bariatric surgery at the Municipal Hospital of Dresden-Neustadt, Germany, were used. Healthy controls were selected from the Study of Health in Pomerania. Non-fasting, spontaneous urine samples were collected, (1)H NMR spectroscopic analysis was performed, and metabolites were quantified (Chenomx NMR suite). Orthogonal projections to latent structures discriminant analysis (OPLS-DA) models were carried out (pre-operative versus controls, and post-operative versus controls). On the basis of the urine metabolome separations between pre-operative (predictive ability Q2Y=85.6%; total explained variance R2X=58.3%), or post-operative (Q2Y=82.1%; R2X=44.4%) and controls were possible. Metabolites including hippuric acid, 3-hydroxybutyrate, 2-hydroxyisobutyrate, and trigonelline, were altered among patients. In obese patients, 2-hydroxyisobutyrate levels were higher, whereas trigonelline and hippuric acid levels were lower than in controls. The highest levels of 3-hydroxybutyrate were found in post-operative samples, whereas the metabolite was not present in controls, and only at low levels in pre-operative samples. In conclusion, we demonstrated that the urinary metabotype differs between obese patients and healthy controls. The metabolic alterations identified after bariatric procedures increase our knowledge about the metabolic traits associated with weight reduction. Whether urinary metabotypes might be used for early prediction of a successful bariatric procedure should be evaluated in long-term observations.

Introduction of virtual microscopy in routine surgical pathology--a hypothesis and personal view from Europe

The technology of whole image acquisition from histological glass slides (Virtual slides, (VS)) and its associated software such as image storage, viewers, and virtual microscopy (VM), has matured in the recent years. There is an ongoing discussion whether to introduce VM into routine diagnostic surgical pathology (tissue-based diagnosis) or not, and if these are to be introduced how best to do this. The discussion also centres around how to substantially define the mandatory standards and working conditions related to introducing VM. This article briefly describes some hypotheses alongside our perspective and that of several of our European colleagues who have experienced VS and VM either in research or routine praxis. After consideration of the different opinions and published data the following statements can be derived: 1. Experiences from static and remote telepathology as well as from daily routine diagnoses, confirm that VM is a diagnostic tool that can be handled with the same diagnostic accuracy as conventional microscopy; at least no statistically significant differences (p > 0.05) exist. 2. VM possesses several practical advantages in comparison to conventional microscopy; such as digital image storage and retrieval and contemporary display of multiple images (acquired from different stains, and/or different cases). 3. VM enables fast and efficient feedback between the pathologist and the laboratory in terms of ordered additional stains, automated access to the latest research for references, and fast consultation with outstanding telepathology experts. 4. Industry has already invested “big money” into this technology which certainly will be of influence in its future development. The main constraints against VM include the questionable reimbursement of the initial investment, the missing direct and short term financial benefit, and the loss of potential biological identity between the patient and the examined tissue. This article tries to analyze and evaluate the factors that influence the implementation of VM into routine tissue-based diagnosis, for example in combination with predictive diagnosis. It focuses on describing the advantages of modern and innovative electronically based communication technology.